Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 509064
Title Response variable selection in principal response curves using permutation testing
Author(s) Vendrig, Nadia J.; Hemerik, Lia; Braak, Cajo J.F. Ter
Source Aquatic Ecology 51 (2017)1. - ISSN 1386-2588 - p. 131 - 143.
DOI http://dx.doi.org/10.1007/s10452-016-9604-1
Department(s) Biometris (WU MAT)
PE&RC
Biometris (PPO/PRI)
Publication type Refereed Article in a scientific journal
Publication year 2017
Keyword(s) longitudinal data - multivariate analysis - multivariate time series - permutation testing - Principal response curves - variable selection
Abstract Principal response curves analysis (PRC) is widely applied to experimental multivariate longitudinal data for the study of time-dependent treatment effects on the multiple outcomes or response variables (RVs). Often, not all of the RVs included in such a study are affected by the treatment and RV-selection can be used to identify those RVs and so give a better estimate of the principal response. We propose four backward selection approaches, based on permutation testing, that differ in whether coefficient size is used or not in ranking the RVs. These methods are expected to give a more robust result than the use of a straightforward cut-off value for coefficient size. Performance of all methods is demonstrated in a simulation study using realistic data. The permutation testing approach that uses information on coefficient size of RVs speeds up the algorithm without affecting its performance. This most successful permutation testing approach removes roughly 95 % of the RVs that are unaffected by the treatment irrespective of the characteristics of the data set and, in the simulations, correctly identifies up to 97 % of RVs affected by the treatment.
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